An Automotive Case Study on the Limits of Approximation for Object Detection
| Autor: | Martí Caro, Hamid Tabani, Jaume Abella, Francesc Moll, Enric Morancho, Ramon Canal, Josep Altet, Antonio Calomarde, Francisco J. Cazorla, Antonio Rubio, Pau Fontova, Jordi Fornt |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica, Barcelona Supercomputing Center, Universitat Politècnica de Catalunya. EFRICS - Efficient and Robust Integrated Circuits and Systems, Universitat Politècnica de Catalunya. PM - Programming Models, Universitat Politècnica de Catalunya. CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Vehicles autònoms Autonomous vehicles Ordinadors immersos Sistemes d' Embedded computer systems Neural networks (Computer science) Energy efficiency Hardware and Architecture Hardware Architecture (cs.AR) Neural network application Autonomous driving Xarxes neuronals (Informàtica) Computer Science - Hardware Architecture Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] Software |
| DOI: | 10.48550/arxiv.2304.06327 |
| Popis: | The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in domains such as autonomous driving. Moreover, sporadic misdetections are irrelevant since confidence on detections is typically averaged across consecutive frames, and detection devices (e.g. cameras, LiDARs) are often redundant, thus providing fault tolerance. This paper exploits such intrinsic fault tolerance of the CBOD process, and assesses in an automotive case study to what extent CBOD can tolerate approximation coming from multiple sources such as lower precision arithmetic, approximate arithmetic units, and even random faults due to, for instance, low voltage operation. We show that the accuracy impact of those sources of approximation is within 1% of the baseline even when considering the three approximate domains simultaneously, and hence, multiple sources of approximation can be exploited to build highly efficient accelerators for CBOD in cars. This work is partially funded by the DRAC project, which is co-financed by the European Union Regional Development Fund within the framework of the ERDF Operational Program of Catalonia 2014–2020 with a grant of 50% of total cost eligible. This work has also been partially supported by the Spanish Ministry of Science and Innovation under grant PID2019-107255GB. |
| Databáze: | OpenAIRE |
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